Update app.py

app.py

@@ -1,83 +1,83 @@
-import streamlit as st
-from sentence_transformers import SentenceTransformer
-from qdrant_client import QdrantClient
-import google.generativeai as genai
-
-# Qdrant details
-QDRANT_URL = "https://807708a6-1d41-4ecb-a1f3-8a41fcd48ec3.us-east4-0.gcp.cloud.qdrant.io:6333"
-QDRANT_API_KEY = "J3LJcoG3q_njIvu9OzjooR2VBD-tx_Zz553gGwMoUD_xzdYz1tFufA"
-QDRANT_COLLECTION_NAME = "courses-data"
-
-# Google Gemini API details
-GEMINI_API_KEY = "API KEY"
-genai.configure(api_key=GEMINI_API_KEY)
-model = genai.GenerativeModel("gemini-1.5-flash")
-
-# Initialize Qdrant client
-qdrant_client = QdrantClient(url=QDRANT_URL, prefer_grpc=True, api_key=QDRANT_API_KEY)
-
-# Load the SentenceTransformer model
-embedder = SentenceTransformer('all-MiniLM-L6-v2')  # Vector size = 384
-
-
-def vector_search(query, collection_name, top_k):
-    """Perform a vector search on the Qdrant collection."""
-    query_vector = embedder.encode(query).tolist()
-    search_result = qdrant_client.search(
-        collection_name=collection_name,
-        query_vector=query_vector,
-        limit=top_k
-    )
-    results = []
-    for result in search_result:
-        chunk_text = result.payload.get('page_content', 'No text found')
-        results.append(chunk_text)
-    return results
-
-
-def gemini(query, chunks):
-    """Generates an answer using Google's Generative AI (Gemini)."""
-    context = "\n".join([f"{i+1}. {chunk}" for i, chunk in enumerate(chunks)])
-    prompt = f"""
-    You are a highly knowledgeable assistant. Based on the given context, please provide a well-crafted answer to the query below. Use the provided information from the context as reference material.
-    
-    ### Context:
-    {context}
-    
-    ### Query:
-    {query}
-    
-    Based on the context, provide a list of courses - course names and a short description.
-    Provide a concise, clear, and informative response based on the query.
-    """
-    # Make the request to generate text
-    response = model.generate_content(prompt)
-
-    # Check if the response contains valid content
-    if response.candidates and len(response.candidates) > 0:
-        return response.text    # Return the generated text as a string
-    else:
-        return "No valid content was returned. Please adjust your prompt or try again."
-
-
-def getResult(input_query):
-    context = vector_search(input_query, QDRANT_COLLECTION_NAME, top_k=5)
-    return gemini(input_query, context)
-
-
-# Streamlit App
-st.title("Course Finder using RAG System")
-st.write("Search for courses using a query. The system retrieves and generates relevant course details.")
-
-# Search bar for user input
-query = st.text_input("Enter your query:", "")
-
-# Display the result when the user enters a query
-if st.button("Search"):
-    if query.strip():
-        with st.spinner("Searching and generating results..."):
-            result = getResult(query)
-        st.subheader("Results:")
-        st.write(result)
-    else:
-        st.warning("Please enter a valid query!")
+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from qdrant_client import QdrantClient
+import google.generativeai as genai
+
+# Qdrant details
+QDRANT_URL = "https://807708a6-1d41-4ecb-a1f3-8a41fcd48ec3.us-east4-0.gcp.cloud.qdrant.io:6333"
+QDRANT_API_KEY = "J3LJcoG3q_njIvu9OzjooR2VBD-tx_Zz553gGwMoUD_xzdYz1tFufA"
+QDRANT_COLLECTION_NAME = "courses-data"
+
+# Google Gemini API details
+GEMINI_API_KEY = "API KEY"
+genai.configure(api_key=GEMINI_API_KEY)
+model = genai.GenerativeModel("gemini-1.5-flash")
+
+# Initialize Qdrant client
+qdrant_client = QdrantClient(url=QDRANT_URL, prefer_grpc=False, api_key=QDRANT_API_KEY)
+
+# Load the SentenceTransformer model
+embedder = SentenceTransformer('all-MiniLM-L6-v2')  # Vector size = 384
+
+
+def vector_search(query, collection_name, top_k):
+    """Perform a vector search on the Qdrant collection."""
+    query_vector = embedder.encode(query).tolist()
+    search_result = qdrant_client.search(
+        collection_name=collection_name,
+        query_vector=query_vector,
+        limit=top_k
+    )
+    results = []
+    for result in search_result:
+        chunk_text = result.payload.get('page_content', 'No text found')
+        results.append(chunk_text)
+    return results
+
+
+def gemini(query, chunks):
+    """Generates an answer using Google's Generative AI (Gemini)."""
+    context = "\n".join([f"{i+1}. {chunk}" for i, chunk in enumerate(chunks)])
+    prompt = f"""
+    You are a highly knowledgeable assistant. Based on the given context, please provide a well-crafted answer to the query below. Use the provided information from the context as reference material.
+    
+    ### Context:
+    {context}
+    
+    ### Query:
+    {query}
+    
+    Based on the context, provide a list of courses - course names and a short description.
+    Provide a concise, clear, and informative response based on the query.
+    """
+    # Make the request to generate text
+    response = model.generate_content(prompt)
+
+    # Check if the response contains valid content
+    if response.candidates and len(response.candidates) > 0:
+        return response.text    # Return the generated text as a string
+    else:
+        return "No valid content was returned. Please adjust your prompt or try again."
+
+
+def getResult(input_query):
+    context = vector_search(input_query, QDRANT_COLLECTION_NAME, top_k=5)
+    return gemini(input_query, context)
+
+
+# Streamlit App
+st.title("Course Finder using RAG System")
+st.write("Search for courses using a query. The system retrieves and generates relevant course details.")
+
+# Search bar for user input
+query = st.text_input("Enter your query:", "")
+
+# Display the result when the user enters a query
+if st.button("Search"):
+    if query.strip():
+        with st.spinner("Searching and generating results..."):
+            result = getResult(query)
+        st.subheader("Results:")
+        st.write(result)
+    else:
+        st.warning("Please enter a valid query!")